B.Tech in Computer Science Engineering (Data Science)

In the second semester, students deepen their understanding of computational principles and data handling. Data Structures and Algorithms focus on efficient data organization and processing techniques. Digital Logic Design covers the principles of electronic circuits, while Computer Organization examines computer architecture. Introduction to Data Analytics explores basic data analysis methods. Engineering Mathematics II builds on mathematical concepts, and Environmental Science provides insights into the intersection of technology and environmental impact.

The third semester emphasizes practical data management and analysis. Operating Systems explores the software that manages hardware resources. Database Management Systems focuses on data storage and retrieval techniques. Statistical Methods for Data Science covers statistical techniques for data interpretation. Machine Learning Fundamentals introduces the basics of machine learning algorithms and their applications. Probability and Statistics are applied to data analysis, and Technical English enhances students’ ability to communicate complex ideas effectively.

In the fourth semester, students tackle more advanced topics in data science. Web Technologies cover the development and deployment of data-driven web applications. Big Data Technologies introduces tools and techniques for handling large-scale data. Data Mining Techniques focus on extracting useful information from large datasets. Predictive Analytics covers methods for forecasting future trends based on historical data. Computer Networks provide understanding of network principles, and students select their first Elective.

The fifth semester delves into specialized areas of data science. Advanced Machine Learning explores more complex machine learning models and techniques. Data Visualization teaches methods for presenting data insights effectively. Natural Language Processing covers techniques for analyzing and processing human language data. Deep Learning introduces advanced neural networks and their applications. Students select their second Elective and an Open Elective I, expanding their academic breadth.

The sixth semester includes hands-on projects and advanced topics. Artificial Intelligence covers intelligent systems and their applications. Data Science Ethics and Privacy addresses the ethical considerations and privacy issues related to data handling. Data Warehousing focuses on the design and management of large-scale data storage systems. Students select their third Elective and an Open Elective II. A Minor Project allows students to apply their knowledge to practical data science challenges.

In the seventh semester, students work on integrating and applying their knowledge. Big Data Analytics examines techniques for analyzing vast amounts of data. IoT Data Management explores data handling in the context of the Internet of Things. Cloud Computing for Data Science covers using cloud services for data processing and storage. Students work on their Major Project Part I and select their fourth Elective and an Open Elective III.

The final semester culminates with comprehensive project work and industry experience. Major Project Part II involves completing and presenting a significant project, showcasing the culmination of learning throughout the program. Professional Ethics in Data Science covers ethical issues specific to the data science field. Industry Internship provides real-world experience and professional exposure. Students select their fifth Elective and an Open Elective IV, rounding out their education with diverse learning opportunities.